As Tropical Storm Melissa swirled south of Haiti, weather expert Philippe Papin felt certain it would soon grow into a major tropical system.
Serving as lead forecaster on duty, he forecasted that in a single day the weather system would intensify into a severe hurricane and start shifting in the direction of the coast of Jamaica. No forecaster had previously made such a bold prediction for quick intensification.
However, Papin possessed a secret advantage: artificial intelligence in the guise of Google’s new DeepMind cyclone prediction system – released for the initial occasion in June. And, as predicted, Melissa did become a storm of astonishing strength that tore through Jamaica.
Meteorologists are increasingly leaning hard on the AI system. During 25 October, Papin explained in his official briefing that the AI tool was a key factor for his confidence: “Roughly 40/50 Google DeepMind simulation runs indicate Melissa becoming a Category 5 hurricane. Although I am not ready to forecast that intensity at this time due to path variability, that remains a possibility.
“It appears likely that a period of quick strengthening is expected as the system drifts over exceptionally hot ocean waters which is the most extreme oceanic heat content in the entire Atlantic basin.”
Google DeepMind is the pioneer artificial intelligence system dedicated to tropical cyclones, and currently the initial to beat standard meteorological experts at their specialty. Across all tropical systems this season, the AI is top-performing – even beating human forecasters on path forecasts.
The hurricane ultimately struck in Jamaica at maximum strength, one of the strongest landfalls ever documented in almost 200 years of record-keeping across the region. The confident prediction likely gave people in Jamaica additional preparation time to get ready for the catastrophe, possibly saving lives and property.
Google’s model operates through spotting patterns that traditional time-intensive physics-based prediction systems may overlook.
“They do it far faster than their physics-based cousins, and the processing requirements is more affordable and time consuming,” said Michael Lowry, a ex forecaster.
“What this hurricane season has demonstrated in short order is that the recent AI weather models are competitive with and, in some cases, more accurate than the slower traditional weather models we’ve traditionally leaned on,” Lowry said.
It’s important to note, Google DeepMind is an instance of machine learning – a technique that has been employed in data-heavy sciences like weather science for years – and is distinct from creative artificial intelligence like ChatGPT.
AI training processes large datasets and pulls out patterns from them in a manner that its model only takes a few minutes to come up with an answer, and can do so on a standard PC – in strong contrast to the primary systems that governments have used for decades that can require many hours to run and need the largest high-performance systems in the world.
Nevertheless, the fact that the AI could outperform previous top-tier legacy models so rapidly is truly remarkable to weather scientists who have dedicated their lives trying to predict the world’s strongest weather systems.
“I’m impressed,” commented James Franklin, a retired forecaster. “The data is sufficient that it’s evident this is not just beginner’s luck.”
Franklin noted that although the AI is beating all other models on forecasting the future path of storms globally this year, like many AI models it sometimes errs on high-end intensity predictions wrong. It struggled with another storm earlier this year, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.
During the next break, he stated he intends to talk with the company about how it can make the AI results even more helpful for forecasters by offering extra under-the-hood data they can use to assess exactly why it is producing its answers.
“The one thing that nags at me is that while these predictions seem to be highly accurate, the results of the system is essentially a black box,” said Franklin.
Historically, no a private, for-profit company that has produced a high-performance forecasting system which grants experts a view of its methods – in contrast to most other models which are offered free to the general audience in their full form by the authorities that designed and maintain them.
Google is not the only one in starting to use artificial intelligence to address difficult meteorological problems. The authorities are developing their respective artificial intelligence systems in the works – which have demonstrated better performance over previous non-AI versions.
Future developments in AI weather forecasts appear to involve startup companies tackling formerly tough-to-solve problems such as sub-seasonal outlooks and improved advance warnings of severe weather and flash flooding – and they are receiving federal support to pursue this. One company, WindBorne Systems, is also deploying its own atmospheric sensors to address deficiencies in the US weather-observing network.
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